Factorial designs and analysis of covariance models; builds a linear model to include main-effects and interactions for categorical predictors (to a specified degree, e.g., two-way effects, three-way effects, etc.). Both univariate (single continuous dependent variable) and multivariate (multiple continuous dependent variables) designs can be analyzed. Default results include the ANOVA/ANCOVA (MANOVA/MANCOVA) table; set the Level of detail parameter to All results to request tables of means and other statistics.

**Model and
Estimation**

**Parameterization
of effects**. Specifies either the sigma-restricted model or
the overparameterized model; the sigma restricted parameterization is
the default.

**Factorial to degree**. Specifies the factorial degree of the design
to be tested; Statistica will construct a factorial design
for all categorical predictors up to the specified degree (i.e., by default
up to degree 2, so that the final model will include all factor main effects
and two-way interactions for categorical predictors).

**Type of
sums of squares**. Specifies how to construct the hypotheses
for the tests of main effects and interactions. Note: Type IV sums of
squares are not available for sigma-restricted parameterization; Type
VI sums of squares are not available for overparameterized parameterization
of categorical factor effects.

**Intercept**.
Specifies whether the intercept (constant) is to be included in the model.

**Lack of
fit**. Requests the computation of pure error for testing the
lack-of-fit hypothesis.

**Sweep delta
1.E-**. Specifies the negative exponent for a base-10 constant
Delta (delta = 10^-sdelta); the default value is 7. Delta is used (1)
in sweeping, to detect redundant columns in the design matrix, and (2)
for evaluating the estimability of hypotheses; specifically a value of
2*delta is used for the estimability check.

**Inverse
delta 1.E-**. Specifies the negative exponent for a base-10 constant
Delta (delta = 10^-idelta); the default value is 12. Delta is used to
check for matrix singularity in matrix inversion calculations.

**Results**

**Detail of computed results reported**. Specifies the detail of
computed results reported. If All results is requested, Statistica
will also report all univariate results (for multivariate designs), descriptive
statistics, details about the design terms, the whole-model R, regression
coefficients, and the least-squares means for all effects. Residual and
predicted statistics (for observations) can be requested as options.

**Least square
means**. Creates the expected marginal means, given the current
model; either all marginal means tables can be computed, or only the means
for the highest-order effect of the factorial design.

**Post Hoc
Tests**. Performs post-hoc comparisons between the means in the
design.

**Tests homogeneity
of variances**. Tests the homogeneity of variances/covariances
assumption. One of the assumptions of univariate ANOVA is that the variances
are equal (homogeneous) across the cells of the between-groups design.
In the multivariate case (MANOVA), this assumption applies to the variance/covariance
matrix of dependent variables (and covariates).

**Plots of
means vs. std. dev**. Plots the (unweighted) marginal means (see
also the Means tab) for the selected Variables against the standard deviations.

**Contrast
coefficients**. Specifies your contrasts for least squares means;
consult the Electronic Manual for syntax details.

**Residual
Analysis**

**Residual analysis**. In addition to the predicted, observed, and
residual values, Statistica will compute the (default) 95%
Prediction intervals and 95% Confidence limits, the Standardized predicted
and Standardized residual score, the Leverage values, the Deleted residual
and Studentized deleted residual scores, Mahalanobis and Cook distance
scores, the DFFITS statistic, and the Standardized DFFITS statistic.

**Normal probability
plot**. Normal probability plot of residuals.

**Generates
data source, if N for input less than**. Generates a data source
for further analyses with other Data Miner nodes if the input data source
has fewer than k observations, as specified in this edit field; note that
parameter k (number of observations) will be evaluated against the number
of observations in the input data source, not the number of valid or selected
observations.

**Deployment. **Deployment is
available if the Statistica installation is licensed for this feature.

**Generates
C/C++ code**. Generates C/C++ code for deployment of predictive
model (for a single dependent variable only).

**Generates SVB code**. Generates Statistica Visual
Basic code for deployment of predictive model (for a single dependent
variable only).

**Generates
PMML code**. Generates PMML (Predictive Models Markup Language)
code for deployment of predictive model (for a single dependent variable
only). This code can be used via the Rapid Deployment options to efficiently
compute predictions for (score) large data sets.

**Saves C/C++
code**. Save C/C++ code for deployment of predictive model (for
a single dependent variable only).

**File name
for C/C code**. Specify the name and location of the file where
to save the (C/C++) deployment code information.

**Saves SVB code**. Save Statistica Visual Basic code
for deployment of predictive model (for a single dependent variable only).

**File name
for SVB code**. Specify the name and location of the file where
to save the (SVB/VB) deployment code information.

**Saves PMML
code**. Saves PMML (Predictive Models Markup Language) code for
deployment of predictive model (for a single dependent variable only).
This code can be used via the Rapid Deployment options to efficiently
compute predictions for (score) large data sets.

**File name
for PMML (XML) code**. Specify the name and location of the file
where to save the (PMML/XML) deployment code information.